We present a novel experiment and perspective on human adaptive control. In our experiment, participants repeatedly adjust, zero, one or two variables with the goal of controlling a third variable, targeting a moving reward region. Across tasks, we vary the function that maps the control variables to the target variable, and use computational modeling to examine how participants represent and solve the tasks. While broadly successful, we find evidence suggesting that participants fall back on projecting a locally linear monotonic relationships, while also taking control actions that are conservative, preferring to adjust one variable rather than both relative to their previous action. We suggest that this allows for robust performance even when interacting with nonlinear non-monotonic functions.